

'''simple docstring'''
import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to properly calculate the metrics on the
# validation dataset when in a distributed system, and builds off the
# `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


_lowerCamelCase      					=     16
_lowerCamelCase      					=     32


def  a__       (    _SCREAMING_SNAKE_CASE   :       Accelerator	,				_SCREAMING_SNAKE_CASE   :       int = 16				)     ->					int:


	"""simple docstring"""





	UpperCAmelCase_     :      Union[str, Any]						=  AutoTokenizer.from_pretrained("bert-base-cased"				)
	UpperCAmelCase_     :      List[Any]						=  load_dataset("glue"	,				"mrpc"				)

	def tokenize_function(_SCREAMING_SNAKE_CASE   :       Optional[int]				):
		# max_length=None => use the model max length (it's actually the default)
		UpperCAmelCase_     :      Union[str, Any]						=  tokenizer(examples["sentence1"]	,				examples["sentence2"]	,				truncation=_SCREAMING_SNAKE_CASE	,				max_length=_SCREAMING_SNAKE_CASE				)
		return outputs

	# Apply the method we just defined to all the examples in all the splits of the dataset
	# starting with the main process first:
	with accelerator.main_process_first():
		UpperCAmelCase_     :      Optional[Any]						=  datasets.map(
		    _SCREAMING_SNAKE_CASE	,				batched=_SCREAMING_SNAKE_CASE	,				remove_columns=["idx", "sentence1", "sentence2"]	,				)

	# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
	# transformers library
	UpperCAmelCase_     :      int						=  tokenized_datasets.rename_column("label"	,				"labels"				)

	def collate_fn(_SCREAMING_SNAKE_CASE   :       Optional[int]				):
		# On TPU it's best to pad everything to the same length or training will be very slow.
		UpperCAmelCase_     :      int						=  1_28 if accelerator.distributed_type == DistributedType.TPU else None
		# When using mixed precision we want round multiples of 8/16
		if accelerator.mixed_precision == "fp8":
			UpperCAmelCase_     :      List[Any]						=  16
		elif accelerator.mixed_precision != "no":
			UpperCAmelCase_     :      Optional[int]						=  8
		else:
			UpperCAmelCase_     :      str						=  None

		return tokenizer.pad(
		    _SCREAMING_SNAKE_CASE	,				padding="longest"	,				max_length=_SCREAMING_SNAKE_CASE	,				pad_to_multiple_of=_SCREAMING_SNAKE_CASE	,				return_tensors="pt"	,				)

	# Instantiate dataloaders.
	UpperCAmelCase_     :      Optional[Any]						=  DataLoader(
	    tokenized_datasets["train"]	,				shuffle=_SCREAMING_SNAKE_CASE	,				collate_fn=_SCREAMING_SNAKE_CASE	,				batch_size=_SCREAMING_SNAKE_CASE				)
	UpperCAmelCase_     :      Any						=  DataLoader(
	    tokenized_datasets["validation"]	,				shuffle=_SCREAMING_SNAKE_CASE	,				collate_fn=_SCREAMING_SNAKE_CASE	,				batch_size=_SCREAMING_SNAKE_CASE				)

	return train_dataloader, eval_dataloader


# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""", None) == "1":
					from accelerate.test_utils.training import mocked_dataloaders

					_lowerCamelCase      					=     mocked_dataloaders  # noqa: F811


def  a__       (    _SCREAMING_SNAKE_CASE   :       str	,				_SCREAMING_SNAKE_CASE   :       Optional[Any]				)     ->					Dict:


	"""simple docstring"""





	if os.environ.get("TESTING_MOCKED_DATALOADERS"	,				_SCREAMING_SNAKE_CASE				) == "1":
		UpperCAmelCase_     :      Any						=  2
	# Initialize accelerator
	UpperCAmelCase_     :      Union[str, Any]						=  Accelerator(cpu=args.cpu	,				mixed_precision=args.mixed_precision				)
	# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
	UpperCAmelCase_     :      str						=  config["lr"]
	UpperCAmelCase_     :      Dict						=  int(config["num_epochs"]				)
	UpperCAmelCase_     :      str						=  int(config["seed"]				)
	UpperCAmelCase_     :      List[str]						=  int(config["batch_size"]				)

	UpperCAmelCase_     :      Union[str, Any]						=  evaluate.load("glue"	,				"mrpc"				)

	# If the batch size is too big we use gradient accumulation
	UpperCAmelCase_     :      Dict						=  1
	if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
		UpperCAmelCase_     :      List[Any]						=  batch_size // MAX_GPU_BATCH_SIZE
		UpperCAmelCase_     :      List[str]						=  MAX_GPU_BATCH_SIZE

	set_seed(_SCREAMING_SNAKE_CASE				)
	UpperCAmelCase_					,					UpperCAmelCase_     :      Optional[int]						=  get_dataloaders(_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE				)
	# Instantiate the model (we build the model here so that the seed also control new weights initialization)
	UpperCAmelCase_     :      Tuple						=  AutoModelForSequenceClassification.from_pretrained("bert-base-cased"	,				return_dict=_SCREAMING_SNAKE_CASE				)

	# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
	# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
	# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
	UpperCAmelCase_     :      Any						=  model.to(accelerator.device				)

	# Instantiate optimizer
	UpperCAmelCase_     :      Optional[Any]						=  AdamW(params=model.parameters()	,				lr=_SCREAMING_SNAKE_CASE				)

	# Instantiate scheduler
	UpperCAmelCase_     :      Union[str, Any]						=  get_linear_schedule_with_warmup(
	    optimizer=_SCREAMING_SNAKE_CASE	,				num_warmup_steps=1_00	,				num_training_steps=(len(_SCREAMING_SNAKE_CASE				) * num_epochs) // gradient_accumulation_steps	,				)

	# Prepare everything
	# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
	# prepare method.
	UpperCAmelCase_					,					UpperCAmelCase_					,					UpperCAmelCase_					,					UpperCAmelCase_					,					UpperCAmelCase_     :      str						=  accelerator.prepare(
	    _SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE				)

	# Now we train the model
	for epoch in range(_SCREAMING_SNAKE_CASE				):
		model.train()
		for step, batch in enumerate(_SCREAMING_SNAKE_CASE				):
			# We could avoid this line since we set the accelerator with `device_placement=True`.
			batch.to(accelerator.device				)
			UpperCAmelCase_     :      List[Any]						=  model(**_SCREAMING_SNAKE_CASE				)
			UpperCAmelCase_     :      Union[str, Any]						=  outputs.loss
			UpperCAmelCase_     :      Union[str, Any]						=  loss / gradient_accumulation_steps
			accelerator.backward(_SCREAMING_SNAKE_CASE				)
			if step % gradient_accumulation_steps == 0:
				optimizer.step()
				lr_scheduler.step()
				optimizer.zero_grad()

		model.eval()
		UpperCAmelCase_     :      Dict						=  0
		for step, batch in enumerate(_SCREAMING_SNAKE_CASE				):
			# We could avoid this line since we set the accelerator with `device_placement=True`.
			batch.to(accelerator.device				)
			with torch.no_grad():
				UpperCAmelCase_     :      Optional[int]						=  model(**_SCREAMING_SNAKE_CASE				)
			UpperCAmelCase_     :      List[Any]						=  outputs.logits.argmax(dim=-1				)
			UpperCAmelCase_					,					UpperCAmelCase_     :      List[str]						=  accelerator.gather((predictions, batch["labels"])				)
			# New Code #
			# First we check if it's a distributed system
			if accelerator.use_distributed:
				# Then see if we're on the last batch of our eval dataloader
				if step == len(_SCREAMING_SNAKE_CASE				) - 1:
					# Last batch needs to be truncated on distributed systems as it contains additional samples
					UpperCAmelCase_     :      Union[str, Any]						=  predictions[: len(eval_dataloader.dataset				) - samples_seen]
					UpperCAmelCase_     :      Optional[Any]						=  references[: len(eval_dataloader.dataset				) - samples_seen]
				else:
					# Otherwise we add the number of samples seen
					samples_seen += references.shape[0]
            # All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
            # accelerator.gather_for_metrics((predictions, batch["labels"]))
			metric.add_batch(
			    predictions=_SCREAMING_SNAKE_CASE	,				references=_SCREAMING_SNAKE_CASE	,				)

		UpperCAmelCase_     :      Union[str, Any]						=  metric.compute()
		# Use accelerator.print to print only on the main process.
		accelerator.print(F'''epoch {epoch}:'''	,				_SCREAMING_SNAKE_CASE				)


def  a__       (    )     ->					Union[str, Any]:


	"""simple docstring"""





	UpperCAmelCase_     :      Any						=  argparse.ArgumentParser(description="Simple example of training script."				)
	parser.add_argument(
	    "--mixed_precision"	,				type=_SCREAMING_SNAKE_CASE	,				default=_SCREAMING_SNAKE_CASE	,				choices=["no", "fp16", "bf16", "fp8"]	,				help="Whether to use mixed precision. Choose"
	    "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
	    "and an Nvidia Ampere GPU."	,				)
	parser.add_argument("--cpu"	,				action="store_true"	,				help="If passed, will train on the CPU."				)
	UpperCAmelCase_     :      str						=  parser.parse_args()
	UpperCAmelCase_     :      Union[str, Any]						=  {"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
	training_function(_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE				)


if __name__ == "__main__":
					main()












'''simple docstring'''





import inspect
import unittest

from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch
						from torch import nn

						from transformers import MODEL_MAPPING, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTModel
						from transformers.models.dpt.modeling_dpt import DPT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
						from PIL import Image

						from transformers import DPTImageProcessor


class  snake_case__      :




					def __init__(  self  :		Union[str, Any]				, _A  :		Optional[Any]				, _A  :		Dict=2				, _A  :		Optional[Any]=32				, _A  :		List[str]=16				, _A  :		str=3				, _A  :		List[Any]=True				, _A  :		Optional[int]=True				, _A  :		Optional[int]=32				, _A  :		Optional[Any]=4				, _A  :		Optional[int]=[0, 1, 2, 3]				, _A  :		List[Any]=4				, _A  :		Optional[int]=37				, _A  :		Optional[Any]="gelu"				, _A  :		Optional[Any]=0.1				, _A  :		Union[str, Any]=0.1				, _A  :		Dict=0.02				, _A  :		Optional[Any]=3				, _A  :		Union[str, Any]=[1, 3_84, 24, 24]				, _A  :		int=True				, _A  :		int=None				, )    ->							Tuple:
								UpperCAmelCase_      :       Tuple          =       parent
								UpperCAmelCase_      :       List[str]          =       batch_size
								UpperCAmelCase_      :       Union[str, Any]          =       image_size
								UpperCAmelCase_      :       List[Any]          =       patch_size
								UpperCAmelCase_      :       Dict          =       num_channels
								UpperCAmelCase_      :       str          =       is_training
								UpperCAmelCase_      :       List[str]          =       use_labels
								UpperCAmelCase_      :       Optional[Any]          =       hidden_size
								UpperCAmelCase_      :       str          =       num_hidden_layers
								UpperCAmelCase_      :       int          =       backbone_out_indices
								UpperCAmelCase_      :       str          =       num_attention_heads
								UpperCAmelCase_      :       List[Any]          =       intermediate_size
								UpperCAmelCase_      :       Union[str, Any]          =       hidden_act
								UpperCAmelCase_      :       Dict          =       hidden_dropout_prob
								UpperCAmelCase_      :       Union[str, Any]          =       attention_probs_dropout_prob
								UpperCAmelCase_      :       List[str]          =       initializer_range
								UpperCAmelCase_      :       int          =       num_labels
								UpperCAmelCase_      :       Union[str, Any]          =       backbone_featmap_shape
								UpperCAmelCase_      :       List[str]          =       scope
								UpperCAmelCase_      :       List[str]          =       is_hybrid
								# sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
								UpperCAmelCase_      :       List[Any]          =       (image_size // patch_size) ** 2
								UpperCAmelCase_      :       List[str]          =       num_patches + 1




					def 		A      (  self  :		Dict      )    ->							Optional[int]:
								UpperCAmelCase_      :       Optional[int]          =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

								UpperCAmelCase_      :       List[str]          =       None
								if self.use_labels:
											UpperCAmelCase_      :       Optional[int]          =       ids_tensor([self.batch_size, self.image_size, self.image_size]				, self.num_labels      )

								UpperCAmelCase_      :       Dict          =       self.get_config()

								return config, pixel_values, labels




					def 		A      (  self  :		int      )    ->							Optional[Any]:
								UpperCAmelCase_      :       Optional[Any]          =       {
								    '''global_padding''': '''same''',
								    '''layer_type''': '''bottleneck''',
								    '''depths''': [3, 4, 9],
								    '''out_features''': ['''stage1''', '''stage2''', '''stage3'''],
								    '''embedding_dynamic_padding''': True,
								    '''hidden_sizes''': [96, 1_92, 3_84, 7_68],
								    '''num_groups''': 2,
								}

								return DPTConfig(
								    image_size=self.image_size				, patch_size=self.patch_size				, num_channels=self.num_channels				, hidden_size=self.hidden_size				, num_hidden_layers=self.num_hidden_layers				, backbone_out_indices=self.backbone_out_indices				, num_attention_heads=self.num_attention_heads				, intermediate_size=self.intermediate_size				, hidden_act=self.hidden_act				, hidden_dropout_prob=self.hidden_dropout_prob				, attention_probs_dropout_prob=self.attention_probs_dropout_prob				, is_decoder=_A				, initializer_range=self.initializer_range				, is_hybrid=self.is_hybrid				, backbone_config=_A				, backbone_featmap_shape=self.backbone_featmap_shape				, )




					def 		A      (  self  :		Union[str, Any]				, _A  :		List[Any]				, _A  :		Optional[int]				, _A  :		Optional[int]      )    ->							Union[str, Any]:
								UpperCAmelCase_      :       Optional[int]          =       DPTModel(config=_A      )
								model.to(_A      )
								model.eval()
								UpperCAmelCase_      :       List[str]          =       model(_A      )
								self.parent.assertEqual(result.last_hidden_state.shape				, (self.batch_size, self.seq_length, self.hidden_size)      )




					def 		A      (  self  :		Tuple				, _A  :		str				, _A  :		Tuple				, _A  :		Union[str, Any]      )    ->							List[str]:
								UpperCAmelCase_      :       List[Any]          =       self.num_labels
								UpperCAmelCase_      :       Any          =       DPTForDepthEstimation(_A      )
								model.to(_A      )
								model.eval()
								UpperCAmelCase_      :       int          =       model(_A      )
								self.parent.assertEqual(result.predicted_depth.shape				, (self.batch_size, self.image_size, self.image_size)      )




					def 		A      (  self  :		List[str]				, _A  :		str				, _A  :		Union[str, Any]				, _A  :		int      )    ->							Dict:
								UpperCAmelCase_      :       int          =       self.num_labels
								UpperCAmelCase_      :       Optional[Any]          =       DPTForSemanticSegmentation(_A      )
								model.to(_A      )
								model.eval()
								UpperCAmelCase_      :       Tuple          =       model(_A				, labels=_A      )
								self.parent.assertEqual(
								    result.logits.shape				, (self.batch_size, self.num_labels, self.image_size, self.image_size)      )





					def 		A      (  self  :		Dict      )    ->							Any:
								UpperCAmelCase_      :       Optional[int]          =       self.prepare_config_and_inputs()
								UpperCAmelCase_    ,      UpperCAmelCase_    ,      UpperCAmelCase_      :       List[str]          =       config_and_inputs
								UpperCAmelCase_      :       Tuple          =       {'''pixel_values''': pixel_values}
								return config, inputs_dict






@require_torch
class  snake_case__      (					UpperCamelCase     ,				UpperCamelCase     ,				unittest.TestCase):
					a_        =					(DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
					a_        =					(
					    {
					        "depth-estimation": DPTForDepthEstimation,
					        "feature-extraction": DPTModel,
					        "image-segmentation": DPTForSemanticSegmentation,
					    }
					    if is_torch_available()
					    else {}
					)

					a_        =					False
					a_        =					False
					a_        =					False




					def 		A      (  self  :		Tuple      )    ->							Tuple:
								UpperCAmelCase_      :       Any          =       DPTModelTester(self      )
								UpperCAmelCase_      :       Dict          =       ConfigTester(self				, config_class=_A				, has_text_modality=_A				, hidden_size=37      )




					def 		A      (  self  :		Optional[int]      )    ->							int:
								self.config_tester.run_common_tests()




					@unittest.skip(reason='''DPT does not use inputs_embeds'''      )
					def 		A      (  self  :		Union[str, Any]      )    ->							str:
								pass




					def 		A      (  self  :		str      )    ->							Dict:
								UpperCAmelCase_    ,      UpperCAmelCase_      :       Any          =       self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
											UpperCAmelCase_      :       Union[str, Any]          =       model_class(_A      )
											self.assertIsInstance(model.get_input_embeddings()				, (nn.Module)      )
											UpperCAmelCase_      :       Dict          =       model.get_output_embeddings()
											self.assertTrue(x is None or isinstance(_A				, nn.Linear      )      )




					def 		A      (  self  :		List[Any]      )    ->							Tuple:
								UpperCAmelCase_    ,      UpperCAmelCase_      :       List[str]          =       self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
											UpperCAmelCase_      :       Dict          =       model_class(_A      )
											UpperCAmelCase_      :       Optional[Any]          =       inspect.signature(model.forward      )
											# signature.parameters is an OrderedDict => so arg_names order is deterministic
											UpperCAmelCase_      :       int          =       [*signature.parameters.keys()]

											UpperCAmelCase_      :       Any          =       ['''pixel_values''']
											self.assertListEqual(arg_names[:1]				, _A      )




					def 		A      (  self  :		List[str]      )    ->							str:
								UpperCAmelCase_      :       List[Any]          =       self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*_A      )




					def 		A      (  self  :		str      )    ->							List[str]:
								UpperCAmelCase_      :       List[Any]          =       self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_depth_estimation(*_A      )




					def 		A      (  self  :		str      )    ->							List[Any]:
								UpperCAmelCase_      :       List[str]          =       self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_semantic_segmentation(*_A      )




					def 		A      (  self  :		Tuple      )    ->							str:
								for model_class in self.all_model_classes:
											if model_class.__name__ == "DPTForDepthEstimation":
														continue

											UpperCAmelCase_    ,      UpperCAmelCase_      :       int          =       self.model_tester.prepare_config_and_inputs_for_common()
											UpperCAmelCase_      :       Union[str, Any]          =       True

											if model_class in get_values(_A      ):
														continue

											UpperCAmelCase_      :       Optional[int]          =       model_class(_A      )
											model.to(_A      )
											model.train()
											UpperCAmelCase_      :       Any          =       self._prepare_for_class(_A				, _A				, return_labels=_A      )
											UpperCAmelCase_      :       Tuple          =       model(**_A      ).loss
											loss.backward()




					def 		A      (  self  :		Union[str, Any]      )    ->							Tuple:
								for model_class in self.all_model_classes:
											if model_class.__name__ == "DPTForDepthEstimation":
														continue

											UpperCAmelCase_    ,      UpperCAmelCase_      :       Tuple          =       self.model_tester.prepare_config_and_inputs_for_common()
											UpperCAmelCase_      :       List[Any]          =       False
											UpperCAmelCase_      :       Optional[Any]          =       True

											if model_class in get_values(_A      ) or not model_class.supports_gradient_checkpointing:
														continue
											UpperCAmelCase_      :       int          =       model_class(_A      )
											model.to(_A      )
											model.gradient_checkpointing_enable()
											model.train()
											UpperCAmelCase_      :       int          =       self._prepare_for_class(_A				, _A				, return_labels=_A      )
											UpperCAmelCase_      :       List[str]          =       model(**_A      ).loss
											loss.backward()




					def 		A      (  self  :		Optional[Any]      )    ->							Any:
								UpperCAmelCase_    ,      UpperCAmelCase_      :       List[str]          =       self.model_tester.prepare_config_and_inputs_for_common()

								UpperCAmelCase_      :       List[str]          =       _config_zero_init(_A      )
								for model_class in self.all_model_classes:
											UpperCAmelCase_      :       int          =       model_class(config=_A      )
											# Skip the check for the backbone
											UpperCAmelCase_      :       List[str]          =       []
											for name, module in model.named_modules():
														if module.__class__.__name__ == "DPTViTHybridEmbeddings":
																	UpperCAmelCase_      :       Union[str, Any]          =       [F"{name}.{key}" for key in module.state_dict().keys()]
																	break

											for name, param in model.named_parameters():
														if param.requires_grad:
																	if name in backbone_params:
																				continue
																	self.assertIn(
																	    ((param.data.mean() * 1e9).round() / 1e9).item()				, [0.0, 1.0]				, msg=F"Parameter {name} of model {model_class} seems not properly initialized"				, )




					@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''      )
					def 		A      (  self  :		Tuple      )    ->							Optional[Any]:
								pass




					@slow
					def 		A      (  self  :		Any      )    ->							int:
								for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
											UpperCAmelCase_      :       Any          =       DPTModel.from_pretrained(_A      )
											self.assertIsNotNone(_A      )





					def 		A      (  self  :		Union[str, Any]      )    ->							str:
								# We do this test only for DPTForDepthEstimation since it is the only model that uses readout_type
								UpperCAmelCase_    ,      UpperCAmelCase_      :       Optional[Any]          =       self.model_tester.prepare_config_and_inputs_for_common()
								UpperCAmelCase_      :       Optional[int]          =       '''add'''
								with self.assertRaises(_A      ):
											UpperCAmelCase_      :       Optional[Any]          =       DPTForDepthEstimation(_A      )






def __UpperCAmelCase   (					)    ->   List[Any]:
			UpperCAmelCase_      :       List[Any]          =       Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''		)
			return image







@require_torch
@require_vision
@slow
class  snake_case__      (					unittest.TestCase):




					def 		A      (  self  :		Union[str, Any]      )    ->							List[Any]:
								UpperCAmelCase_      :       List[Any]          =       DPTImageProcessor.from_pretrained('''Intel/dpt-hybrid-midas'''      )
								UpperCAmelCase_      :       Any          =       DPTForDepthEstimation.from_pretrained('''Intel/dpt-hybrid-midas'''      ).to(_A      )

								UpperCAmelCase_      :       Tuple          =       prepare_img()
								UpperCAmelCase_      :       Union[str, Any]          =       image_processor(images=_A				, return_tensors='''pt'''      ).to(_A      )

								# forward pass
								with torch.no_grad():
											UpperCAmelCase_      :       str          =       model(**_A      )
											UpperCAmelCase_      :       List[Any]          =       outputs.predicted_depth

								# verify the predicted depth
								UpperCAmelCase_      :       str          =       torch.Size((1, 3_84, 3_84)      )
								self.assertEqual(predicted_depth.shape				, _A      )

								UpperCAmelCase_      :       Tuple          =       torch.tensor(
								    [[[5.6_437, 5.6_146, 5.6_511], [5.4_371, 5.5_649, 5.5_958], [5.5_215, 5.5_184, 5.5_293]]]      ).to(_A      )

								self.assertTrue(torch.allclose(outputs.predicted_depth[:3, :3, :3] / 1_00				, _A				, atol=1e-4      )      )


